ABC: Optimization using Artificial Bee Colony Algorithm
In metaheuristicOpt: Metaheuristic for Optimization

Description Usage Arguments Details Value References See Also Examples

This is the internal function that implements Artificial Bee Colony Algorithm. It is used to solve continuous optimization tasks. Users do not need to call it directly, but just use metaOpt.

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ABC(FUN, optimType = "MIN", numVar, numPopulation = 40,
  maxIter = 500, rangeVar, cycleLimit = as.integer(numVar *
  numPopulation))

`FUN`	an objective function or cost function,
`optimType`	a string value that represent the type of optimization. There are two option for this arguments: `"MIN"` and `"MAX"`. The default value is `"MIN"`, which the function will do minimization. Otherwise, you can use `"MAX"` for maximization problem. The default value is `"MIN"`.
`numVar`	a positive integer to determine the number variables.
`numPopulation`	a positive integer to determine the number populations. The default value is 40.
`maxIter`	a positive integer to determine the maximum number of iterations. The default value is 500.
`rangeVar`	a matrix (2 \times n) containing the range of variables, where n is the number of variables, and first and second rows are the lower bound (minimum) and upper bound (maximum) values, respectively. If all variable have equal upper bound, you can define `rangeVar` as matrix (2 \times 1).
`cycleLimit`	a positive integer to determine number of times allowed for candidate solution to not move. The default value is `as.integer(numVar * numPopulation)`.

This algorithm was proposed by (Karaboga & Akay, 2009). It inspired by type of bee. They are three types of bee employeed, onlooker and scout. Employed bee work by finding food source. Onlooker bee work by finding better food source other than foods that Employed bee found. Scout bee work by removing abandoned food source. Each candidate solution in ABC algorithm represent as bee and they will move in 3 phases employed, onlooker and scout.

In order to find the optimal solution, the algorithm follow the following steps.

initialize population randomly.
Employed bee phase (Perform local search and greedy algorithm for each candidate solution).
Onlooker bee phase (Perform local search and greedy algorithm for some candidate solutions).
Scout bee phase (Remove abandoned candidate solutions).
If a termination criterion (a maximum number of iterations or a sufficiently good fitness) is met, exit the loop, else back to employed bee phase.

Vector [v1, v2, ..., vn] where n is number variable and vn is value of n-th variable.

Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied mathematics and computation, 214(1), 108-132.

metaOpt

##################################
## Optimizing the sphere function

# define sphere function as objective function
sphere <- function(x){
    return(sum(x^2))
}

## Define parameter
numVar <- 5
rangeVar <- matrix(c(-10,10), nrow=2)

## calculate the optimum solution using artificial bee colony algorithm
resultABC <- ABC(sphere, optimType="MIN", numVar, numPopulation=20,
                 maxIter=100, rangeVar)

## calculate the optimum value using sphere function
optimum.value <- sphere(resultABC)